{"id":6792,"date":"2026-03-23T12:44:35","date_gmt":"2026-03-23T12:44:35","guid":{"rendered":"https:\/\/www.wizbrand.com\/tutorials\/daily-active-users\/"},"modified":"2026-03-23T12:44:35","modified_gmt":"2026-03-23T12:44:35","slug":"daily-active-users","status":"publish","type":"post","link":"https:\/\/www.wizbrand.com\/tutorials\/daily-active-users\/","title":{"rendered":"Daily Active Users: What It Is, Key Features, Benefits, Use Cases, and How It Fits in Analytics"},"content":{"rendered":"\n

Daily Active Users (DAU) is one of the most referenced product and marketing metrics because it answers a simple, high-impact question: how many unique people actively used your product today? In Conversion & Measurement<\/strong>, that \u201ctoday\u201d matters\u2014DAU is sensitive to campaigns, onboarding changes, pricing tests, push notifications, seasonality, outages, and competitive moves. In Analytics<\/strong>, DAU becomes a daily health check that connects acquisition, activation, engagement, and retention into a single, trackable signal.<\/p>\n\n\n\n

Modern Conversion & Measurement<\/strong> strategy relies on fast feedback loops. DAU provides those loops when it\u2019s defined clearly, measured consistently, and interpreted alongside supporting metrics. Used well, DAU helps teams detect early growth, diagnose churn risk, and validate whether marketing and product improvements translate into real usage\u2014not just clicks or installs.<\/p>\n\n\n\n

What Is Daily Active Users?<\/h2>\n\n\n\n

Daily Active Users (DAU)<\/strong> is the number of unique users<\/strong> who perform at least one qualifying activity<\/strong> in a product during a single calendar day<\/strong>. The acronym DAU<\/strong> stands for Daily Active Users<\/em>. The critical detail is that \u201cactive\u201d is not universal\u2014each business must define what actions count as active based on product value.<\/p>\n\n\n\n

At its core, DAU is a de-duplicated user count<\/strong> over a daily time window. If the same person opens your app five times in one day, they still count as one<\/strong> Daily Active User for that day, assuming your identity strategy can recognize them as the same user.<\/p>\n\n\n\n

From a business perspective, DAU is a proxy for:\n– Demand and habit<\/strong> (are users coming back daily?)\n– Product-market fit signals<\/strong> (does usage persist beyond acquisition bursts?)\n– Revenue opportunity<\/strong> (more active users often increases conversion opportunities and ad inventory)<\/p>\n\n\n\n

Within Conversion & Measurement<\/strong>, DAU sits downstream of acquisition and activation and upstream of monetization. It complements conversion metrics by showing whether users are present to convert. Inside Analytics<\/strong>, DAU is a foundational metric for retention analysis, cohort tracking, experimentation readouts, and forecasting.<\/p>\n\n\n\n

Why Daily Active Users Matters in Conversion & Measurement<\/h2>\n\n\n\n

In Conversion & Measurement<\/strong>, it\u2019s easy to over-focus on top-of-funnel numbers\u2014impressions, clicks, sign-ups\u2014without confirming that users actually show up and use the product. Daily Active Users closes that gap by measuring real engagement at a daily cadence.<\/p>\n\n\n\n

DAU matters strategically because it can:\n– Validate campaign quality<\/strong>: A campaign that drives sign-ups but not DAU may be attracting the wrong audience or setting the wrong expectations.\n– Reveal onboarding effectiveness<\/strong>: If activation steps improve, you often see DAU lift before revenue fully follows.\n– Support retention-led growth<\/strong>: For many products, long-term growth depends more on users returning than on constant new acquisition.<\/p>\n\n\n\n

It also creates competitive advantage. Teams that monitor Daily Active Users with strong Analytics<\/strong> hygiene can spot inflection points early\u2014positive (feature adoption) or negative (tracking bugs, performance issues, messaging mismatch)\u2014and react faster than competitors who rely on monthly reports.<\/p>\n\n\n\n

How Daily Active Users Works<\/h2>\n\n\n\n

Daily Active Users is conceptual, but it follows a practical measurement workflow:<\/p>\n\n\n\n

    \n
  1. \n

    Input (user activity + identity)<\/strong>\n – Users perform events (open app, view content, send message, complete task).\n – A user identity is captured (logged-in ID, device ID, hashed identifier, or modeled identity).<\/p>\n<\/li>\n

  2. \n

    Processing (definition + deduplication)<\/strong>\n – You define \u201cactive\u201d (which events qualify, and whether thresholds apply).\n – Events are filtered by date and criteria, then deduplicated to count unique users.<\/p>\n<\/li>\n

  3. \n

    Application (analysis + decision-making)<\/strong>\n – DAU is segmented by channel, campaign, geography, device, plan type, or cohort.\n – Trends are reviewed alongside Conversion & Measurement<\/strong> indicators like signup conversion, activation rate, and purchase rate.<\/p>\n<\/li>\n

  4. \n

    Output (insights + actions)<\/strong>\n – Teams act: adjust targeting, refine onboarding, improve lifecycle messaging, fix product friction, or investigate anomalies.\n – DAU becomes a baseline for experiments: \u201cDid the change increase Daily Active Users without harming retention or conversion?\u201d<\/p>\n<\/li>\n<\/ol>\n\n\n\n

    The metric is only as trustworthy as the definition of \u201cactive,\u201d the identity model, and the consistency of tracking\u2014key considerations in Analytics<\/strong> governance.<\/p>\n\n\n\n

    Key Components of Daily Active Users<\/h2>\n\n\n\n

    A reliable Daily Active Users program typically includes:<\/p>\n\n\n\n

    Clear \u201cactive\u201d definition<\/h3>\n\n\n\n

    Your DAU definition should reflect meaningful value. For a messaging app it might be \u201csent a message,\u201d while for a marketplace it might be \u201cviewed at least 3 listings\u201d or \u201ccontacted a seller.\u201d In Conversion & Measurement<\/strong>, the best definitions align with value moments that predict retention or revenue.<\/p>\n\n\n\n

    Event instrumentation<\/h3>\n\n\n\n

    You need accurate event tracking for the actions that define activity. This includes consistent event names, properties (platform, channel, user type), and timestamp handling across web and app.<\/p>\n\n\n\n

    Identity and deduplication logic<\/h3>\n\n\n\n

    DAU depends on counting unique users<\/strong>, which requires:\n– A stable user ID for logged-in experiences\n– A method for anonymous users (cookies\/device IDs)\n– Rules for merging identities when users log in<\/p>\n\n\n\n

    Data quality and governance<\/h3>\n\n\n\n

    Strong Analytics<\/strong> practice includes validation checks (missing events, sudden spikes), documentation, and version control for tracking changes so DAU remains comparable over time.<\/p>\n\n\n\n

    Team ownership<\/h3>\n\n\n\n

    DAU often spans marketing, product, data, and engineering. Clear ownership prevents definition drift and ensures Conversion & Measurement<\/strong> reporting is consistent.<\/p>\n\n\n\n

    Types of Daily Active Users<\/h2>\n\n\n\n

    Daily Active Users doesn\u2019t have universally standardized \u201ctypes,\u201d but there are important distinctions used in practice:<\/p>\n\n\n\n

    Product-qualified DAU vs. open-app DAU<\/h3>\n\n\n\n
      \n
    • Open-app DAU<\/strong> counts users who merely open the app or visit the site.<\/li>\n
    • Product-qualified DAU<\/strong> counts users who complete a meaningful action (recommended for decision-making in Conversion & Measurement<\/strong>).<\/li>\n<\/ul>\n\n\n\n

      Logged-in DAU vs. total DAU<\/h3>\n\n\n\n
        \n
      • Logged-in DAU<\/strong> is cleaner for identity, segmentation, and retention analysis.<\/li>\n
      • Total DAU<\/strong> includes anonymous users and can be useful for content sites, but is more sensitive to cookie loss and privacy constraints in Analytics<\/strong>.<\/li>\n<\/ul>\n\n\n\n

        New vs. returning DAU<\/h3>\n\n\n\n

        Splitting Daily Active Users into first-time actives vs. returning actives helps diagnose whether growth comes from acquisition or from retention improvements.<\/p>\n\n\n\n

        Segmented DAU (by channel or cohort)<\/h3>\n\n\n\n

        DAU by acquisition source, campaign, geography, or cohort is often more actionable than a single top-line number, especially in Conversion & Measurement<\/strong> programs tied to paid media.<\/p>\n\n\n\n

        Real-World Examples of Daily Active Users<\/h2>\n\n\n\n

        1) Mobile subscription app optimizing onboarding<\/h3>\n\n\n\n

        A meditation app updates onboarding to better match user goals (sleep, focus, stress). The team tracks Daily Active Users among new sign-ups in the first 7 days. DAU rises, and the Analytics<\/strong> team confirms it\u2019s driven by more first-session completions and fewer day-2 drop-offs. In Conversion & Measurement<\/strong>, they attribute improved trial-to-paid conversion partially to higher early DAU.<\/p>\n\n\n\n

        2) B2B SaaS evaluating lead quality from paid campaigns<\/h3>\n\n\n\n

        A SaaS company runs two LinkedIn campaigns: one focused on low-cost clicks, the other on pain-point messaging. Both drive demos, but when the product team reviews Daily Active Users for newly created workspaces, the \u201ccheap clicks\u201d campaign yields far lower DAU. The company shifts budget to the higher-intent campaign because DAU indicates real product engagement, not just form fills\u2014tightening Conversion & Measurement<\/strong> efficiency.<\/p>\n\n\n\n

        3) Content platform diagnosing a traffic spike<\/h3>\n\n\n\n

        A publisher sees a sudden DAU spike after a social post goes viral. In Analytics<\/strong>, they segment Daily Active Users by new vs. returning users and by session depth. The spike is mostly new users with shallow engagement. In Conversion & Measurement<\/strong>, they adjust the landing experience and newsletter prompts to convert that temporary DAU boost into returning daily usage.<\/p>\n\n\n\n

        Benefits of Using Daily Active Users<\/h2>\n\n\n\n

        When measured well, Daily Active Users delivers practical benefits:<\/p>\n\n\n\n

          \n
        • Faster performance feedback<\/strong>: DAU reacts quickly to campaigns, feature releases, and messaging changes, enabling tighter Conversion & Measurement<\/strong> cycles.<\/li>\n
        • Better budget allocation<\/strong>: Pairing DAU with acquisition cost highlights which channels produce users who actually engage.<\/li>\n
        • Improved lifecycle marketing<\/strong>: DAU trends help calibrate push\/email cadence and identify reactivation opportunities.<\/li>\n
        • Stronger user experience decisions<\/strong>: DAU drops can reveal friction, bugs, or confusing UX faster than revenue metrics alone.<\/li>\n
        • Operational clarity<\/strong>: A consistent DAU definition creates a shared \u201cnorth star\u201d signal for cross-functional teams using Analytics<\/strong>.<\/li>\n<\/ul>\n\n\n\n

          Challenges of Daily Active Users<\/h2>\n\n\n\n

          Daily Active Users is powerful, but it has common pitfalls:<\/p>\n\n\n\n

            \n
          • Ambiguous \u201cactive\u201d definitions<\/strong>: If \u201cactive\u201d is too broad (e.g., any page view), DAU inflates and becomes less predictive of value in Conversion & Measurement<\/strong>.<\/li>\n
          • Identity fragmentation<\/strong>: Users switch devices, clear cookies, or browse anonymously; this can overcount unique users and distort DAU in Analytics<\/strong>.<\/li>\n
          • Timezone and date-boundary issues<\/strong>: \u201cDaily\u201d depends on a timezone standard. Mixed timezones can create artificial dips\/spikes.<\/li>\n
          • Bots and fraud<\/strong>: Content sites and freemium products may see bot traffic that inflates DAU unless filtered.<\/li>\n
          • Comparability over time<\/strong>: Tracking changes (event renames, SDK updates) can break historical comparisons unless governed properly.<\/li>\n
          • Vanity metric risk<\/strong>: DAU alone doesn\u2019t guarantee retention, revenue, or satisfaction; it must be interpreted with supporting metrics.<\/li>\n<\/ul>\n\n\n\n

            Best Practices for Daily Active Users<\/h2>\n\n\n\n

            Define \u201cactive\u201d around value<\/h3>\n\n\n\n

            Pick actions that reflect meaningful engagement and correlate with retention or monetization. Document the definition so Conversion & Measurement<\/strong> reporting stays consistent across teams.<\/p>\n\n\n\n

            Standardize time and counting rules<\/h3>\n\n\n\n
              \n
            • Choose a reporting timezone and stick to it.<\/li>\n
            • Count unique users per day<\/strong>, not sessions.<\/li>\n
            • Ensure late-arriving events (mobile offline) are handled consistently in Analytics<\/strong> pipelines.<\/li>\n<\/ul>\n\n\n\n

              Segment before you conclude<\/h3>\n\n\n\n

              Always review Daily Active Users by:\n– New vs. returning\n– Channel\/campaign\n– Platform (web\/iOS\/Android)\n– Geography\nSegmentation turns DAU from a headline into a diagnostic tool.<\/p>\n\n\n\n

              Pair DAU with quality signals<\/h3>\n\n\n\n

              Use DAU alongside activation completion, depth of engagement, and retention cohorts. In Conversion & Measurement<\/strong>, this prevents optimizing for shallow daily visits that don\u2019t convert or retain.<\/p>\n\n\n\n

              Monitor with guardrails<\/h3>\n\n\n\n

              Set anomaly detection or thresholds for sudden changes. A DAU dip could be a tracking issue, an outage, or a real behavior change\u2014fast triage protects decision-making.<\/p>\n\n\n\n

              Use experiments to validate lifts<\/h3>\n\n\n\n

              When you run A\/B tests, evaluate whether changes increase Daily Active Users and whether the lift persists beyond novelty effects.<\/p>\n\n\n\n

              Tools Used for Daily Active Users<\/h2>\n\n\n\n

              Daily Active Users can be measured and operationalized with several tool categories commonly used in Conversion & Measurement<\/strong> and Analytics<\/strong>:<\/p>\n\n\n\n

                \n
              • Analytics tools (event + attribution)<\/strong>: Capture events, define active criteria, and support segmentation by channel and cohort.<\/li>\n
              • Tag management and SDK tooling<\/strong>: Helps standardize instrumentation across web and mobile and reduces tracking inconsistencies.<\/li>\n
              • Data warehouses and pipelines<\/strong>: Store raw events, enable deduplication logic, and power trustworthy DAU calculations at scale.<\/li>\n
              • BI and reporting dashboards<\/strong>: Visualize Daily Active Users trends, build executive reporting, and share segmented views.<\/li>\n
              • CRM and lifecycle automation<\/strong>: Use DAU states (active today vs. inactive) to trigger messaging, win-back campaigns, or onboarding sequences.<\/li>\n
              • Experimentation platforms<\/strong>: Evaluate whether product or marketing changes impact DAU and downstream conversion metrics.<\/li>\n<\/ul>\n\n\n\n

                The key is not the brand of tool, but consistent definitions, identity strategy, and governance so Analytics<\/strong> outputs align with business decisions.<\/p>\n\n\n\n

                Metrics Related to Daily Active Users<\/h2>\n\n\n\n

                Daily Active Users becomes more meaningful when paired with adjacent indicators:<\/p>\n\n\n\n

                  \n
                • WAU\/MAU (Weekly\/Monthly Active Users)<\/strong>: Adds context to daily volatility.<\/li>\n
                • DAU\/MAU ratio (\u201cstickiness\u201d)<\/strong>: Indicates how frequently monthly users return; useful for habit strength.<\/li>\n
                • Retention (D1, D7, D30)<\/strong>: Tells whether DAU growth is durable.<\/li>\n
                • Activation rate<\/strong>: Percentage of new users reaching a \u201cvalue moment\u201d that predicts future DAU.<\/li>\n
                • Sessions per user and engagement depth<\/strong>: Distinguishes shallow DAU from meaningful usage.<\/li>\n
                • Conversion rate (trial-to-paid, lead-to-customer, purchase rate)<\/strong>: Connects Daily Active Users to revenue outcomes in Conversion & Measurement<\/strong>.<\/li>\n
                • ARPU\/ARPPU and LTV<\/strong>: Helps determine whether DAU growth translates into profitable growth.<\/li>\n
                • Churn rate<\/strong>: DAU can decline before churn shows up in billing systems; Analytics<\/strong> can catch early signals.<\/li>\n<\/ul>\n\n\n\n

                  Future Trends of Daily Active Users<\/h2>\n\n\n\n

                  Daily Active Users is evolving as measurement constraints and personalization capabilities change:<\/p>\n\n\n\n

                    \n
                  • Privacy-driven identity shifts<\/strong>: Cookie loss, platform restrictions, and consent requirements will push teams toward stronger first-party identity, modeled conversions, and aggregated reporting\u2014affecting how DAU is counted in Analytics<\/strong>.<\/li>\n
                  • AI-assisted segmentation and anomaly detection<\/strong>: AI will increasingly flag DAU anomalies (tracking breaks vs. real drops) and identify which cohorts drive changes, accelerating Conversion & Measurement<\/strong> response time.<\/li>\n
                  • Personalized lifecycle orchestration<\/strong>: DAU will be used more dynamically to trigger tailored onboarding and retention messaging (e.g., \u201cinactive for 3 days\u201d), improving user experience while reducing waste.<\/li>\n
                  • Focus on quality-adjusted activity<\/strong>: Expect more teams to adopt \u201cqualified DAU\u201d definitions tied to value events rather than simple opens, improving strategic decisions.<\/li>\n
                  • Cross-platform measurement maturity<\/strong>: As users move between web, app, and connected devices, unified identity and consistent event schemas will become more central to trustworthy Daily Active Users reporting.<\/li>\n<\/ul>\n\n\n\n

                    Daily Active Users vs Related Terms<\/h2>\n\n\n\n

                    Daily Active Users vs. Sessions<\/h3>\n\n\n\n
                      \n
                    • Daily Active Users<\/strong> counts unique users who were active today.<\/li>\n
                    • Sessions<\/strong> count visits\/usage instances, and one user can generate multiple sessions.\nIn Conversion & Measurement<\/strong>, sessions help understand frequency, while DAU tracks reach and habit.<\/li>\n<\/ul>\n\n\n\n

                      Daily Active Users vs. Monthly Active Users (MAU)<\/h3>\n\n\n\n
                        \n
                      • DAU<\/strong> is daily engagement; more sensitive to short-term changes.<\/li>\n
                      • MAU<\/strong> is broader and smoother; better for long-range trend analysis.\nIn Analytics<\/strong>, comparing DAU to MAU via DAU\/MAU helps quantify stickiness.<\/li>\n<\/ul>\n\n\n\n

                        Daily Active Users vs. Active Accounts (B2B)<\/h3>\n\n\n\n
                          \n
                        • DAU<\/strong> is people-based (users).<\/li>\n
                        • Active accounts<\/strong> is organization-based (accounts\/companies).\nB2B Conversion & Measurement<\/strong> often needs both: DAU for adoption depth and active accounts for expansion potential.<\/li>\n<\/ul>\n\n\n\n

                          Who Should Learn Daily Active Users<\/h2>\n\n\n\n
                            \n
                          • Marketers<\/strong> need Daily Active Users to judge whether acquisition produces real engagement and to optimize lifecycle journeys within Conversion & Measurement<\/strong>.<\/li>\n
                          • Analysts<\/strong> use DAU as a building block for retention cohorts, forecasting, and experiment measurement in Analytics<\/strong>.<\/li>\n
                          • Agencies<\/strong> benefit from DAU when proving campaign quality beyond top-of-funnel metrics and defending budget decisions with engagement evidence.<\/li>\n
                          • Business owners and founders<\/strong> use Daily Active Users as an early-warning system for growth health and product-market fit.<\/li>\n
                          • Developers and product teams<\/strong> need DAU definitions to instrument events correctly, maintain data quality, and connect releases to measurable outcomes.<\/li>\n<\/ul>\n\n\n\n

                            Summary of Daily Active Users<\/h2>\n\n\n\n

                            Daily Active Users (DAU) measures how many unique users actively engaged with your product in a day, based on a clear definition of \u201cactive.\u201d It matters because it provides fast, actionable insight into engagement health and campaign quality. In Conversion & Measurement<\/strong>, DAU bridges acquisition and monetization by showing whether users show up consistently. In Analytics<\/strong>, it supports segmentation, retention analysis, experimentation, and reliable trend monitoring\u2014provided identity, instrumentation, and governance are handled thoughtfully.<\/p>\n\n\n\n

                            Frequently Asked Questions (FAQ)<\/h2>\n\n\n\n

                            1) What are Daily Active Users, and how do I define \u201cactive\u201d?<\/h3>\n\n\n\n

                            Daily Active Users are unique users who complete at least one qualifying action in a day. Define \u201cactive\u201d as a value-based event (or set of events) that indicates meaningful use\u2014more than a simple open\u2014so it supports real Conversion & Measurement<\/strong> decisions.<\/p>\n\n\n\n

                            2) Is DAU a vanity metric?<\/h3>\n\n\n\n

                            DAU can become a vanity metric if \u201cactive\u201d is defined too loosely or if it\u2019s reviewed without context. Pair Daily Active Users with retention, conversion, and engagement depth in Analytics<\/strong> to ensure the metric reflects value.<\/p>\n\n\n\n

                            3) How is DAU different from WAU and MAU?<\/h3>\n\n\n\n

                            DAU is daily, WAU is weekly, and MAU is monthly unique actives. DAU is best for rapid feedback; MAU is best for broad reach; WAU sits between them. Comparing DAU and MAU (DAU\/MAU) helps quantify stickiness.<\/p>\n\n\n\n

                            4) How do privacy changes affect DAU measurement?<\/h3>\n\n\n\n

                            Privacy changes can reduce identity continuity (cookies, device identifiers, consent). That can increase duplicates or missing users in Analytics<\/strong>. Mitigate by strengthening first-party identity, improving login adoption, and standardizing event schemas.<\/p>\n\n\n\n

                            5) What\u2019s a good DAU number?<\/h3>\n\n\n\n

                            There\u2019s no universal benchmark. A \u201cgood\u201d Daily Active Users level depends on your market, product frequency (daily vs. monthly use cases), and business model. In Conversion & Measurement<\/strong>, the better question is whether DAU is growing profitably and whether retention cohorts are improving.<\/p>\n\n\n\n

                            6) Which teams should own DAU reporting?<\/h3>\n\n\n\n

                            Ownership is typically shared: product defines meaningful activity, engineering implements tracking, and data\/marketing validates reporting. Clear governance ensures DAU stays consistent and trustworthy across Analytics<\/strong> and executive dashboards.<\/p>\n","protected":false},"excerpt":{"rendered":"

                            Daily Active Users (DAU) is one of the most referenced product and marketing metrics because it answers a simple, high-impact question: how many unique people actively used your product today? In **Conversion & Measurement**, that \u201ctoday\u201d matters\u2014DAU is sensitive to campaigns, onboarding changes, pricing tests, push notifications, seasonality, outages, and competitive moves. In **Analytics**, DAU becomes a daily health check that connects acquisition, activation, engagement, and retention into a single, trackable signal.<\/p>\n","protected":false},"author":10235,"featured_media":0,"comment_status":"open","ping_status":"","sticky":false,"template":"","format":"standard","meta":{"_jetpack_memberships_contains_paid_content":false,"footnotes":""},"categories":[1887],"tags":[],"class_list":["post-6792","post","type-post","status-publish","format-standard","hentry","category-analytics"],"jetpack_featured_media_url":"","jetpack_sharing_enabled":true,"_links":{"self":[{"href":"https:\/\/www.wizbrand.com\/tutorials\/wp-json\/wp\/v2\/posts\/6792","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/www.wizbrand.com\/tutorials\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/www.wizbrand.com\/tutorials\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/www.wizbrand.com\/tutorials\/wp-json\/wp\/v2\/users\/10235"}],"replies":[{"embeddable":true,"href":"https:\/\/www.wizbrand.com\/tutorials\/wp-json\/wp\/v2\/comments?post=6792"}],"version-history":[{"count":0,"href":"https:\/\/www.wizbrand.com\/tutorials\/wp-json\/wp\/v2\/posts\/6792\/revisions"}],"wp:attachment":[{"href":"https:\/\/www.wizbrand.com\/tutorials\/wp-json\/wp\/v2\/media?parent=6792"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/www.wizbrand.com\/tutorials\/wp-json\/wp\/v2\/categories?post=6792"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/www.wizbrand.com\/tutorials\/wp-json\/wp\/v2\/tags?post=6792"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}